14,600 research outputs found
Marker based Thermal-Inertial Localization for Aerial Robots in Obscurant Filled Environments
For robotic inspection tasks in known environments fiducial markers provide a
reliable and low-cost solution for robot localization. However, detection of
such markers relies on the quality of RGB camera data, which degrades
significantly in the presence of visual obscurants such as fog and smoke. The
ability to navigate known environments in the presence of obscurants can be
critical for inspection tasks especially, in the aftermath of a disaster.
Addressing such a scenario, this work proposes a method for the design of
fiducial markers to be used with thermal cameras for the pose estimation of
aerial robots. Our low cost markers are designed to work in the long wave
infrared spectrum, which is not affected by the presence of obscurants, and can
be affixed to any object that has measurable temperature difference with
respect to its surroundings. Furthermore, the estimated pose from the fiducial
markers is fused with inertial measurements in an extended Kalman filter to
remove high frequency noise and error present in the fiducial pose estimates.
The proposed markers and the pose estimation method are experimentally
evaluated in an obscurant filled environment using an aerial robot carrying a
thermal camera.Comment: 10 pages, 5 figures, Published in International Symposium on Visual
Computing 201
A review of advances in pixel detectors for experiments with high rate and radiation
The Large Hadron Collider (LHC) experiments ATLAS and CMS have established
hybrid pixel detectors as the instrument of choice for particle tracking and
vertexing in high rate and radiation environments, as they operate close to the
LHC interaction points. With the High Luminosity-LHC upgrade now in sight, for
which the tracking detectors will be completely replaced, new generations of
pixel detectors are being devised. They have to address enormous challenges in
terms of data throughput and radiation levels, ionizing and non-ionizing, that
harm the sensing and readout parts of pixel detectors alike. Advances in
microelectronics and microprocessing technologies now enable large scale
detector designs with unprecedented performance in measurement precision (space
and time), radiation hard sensors and readout chips, hybridization techniques,
lightweight supports, and fully monolithic approaches to meet these challenges.
This paper reviews the world-wide effort on these developments.Comment: 84 pages with 46 figures. Review article.For submission to Rep. Prog.
Phy
Focusing on out-of-focus : assessing defocus estimation algorithms for the benefit of automated image masking
Acquiring photographs as input for an image-based modelling pipeline is less trivial than often assumed. Photographs should be correctly exposed, cover the subject sufficiently from all possible angles, have the required spatial resolution, be devoid of any motion blur, exhibit accurate focus and feature an adequate depth of field. The last four characteristics all determine the " sharpness " of an image and the photogrammetric, computer vision and hybrid photogrammetric computer vision communities all assume that the object to be modelled is depicted " acceptably " sharp throughout the whole image collection. Although none of these three fields has ever properly quantified " acceptably sharp " , it is more or less standard practice to mask those image portions that appear to be unsharp due to the limited depth of field around the plane of focus (whether this means blurry object parts or completely out-of-focus backgrounds). This paper will assess how well-or ill-suited defocus estimating algorithms are for automatically masking a series of photographs, since this could speed up modelling pipelines with many hundreds or thousands of photographs. To that end, the paper uses five different real-world datasets and compares the output of three state-of-the-art edge-based defocus estimators. Afterwards, critical comments and plans for the future finalise this paper
A Survey of Positioning Systems Using Visible LED Lights
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Electrical Impedance Tomography Guided by Digital Twins and Deep Learning for Lung Monitoring
In recent years, there has been an increasing interest in applying electrical impedance tomography (EIT) in lung monitoring due to its advantages of being noninvasive, nonionizing, real time, and functional imaging with no harmful side effects. However, the EIT images reconstructed by traditional algorithms suffer from low spatial resolution. This article proposes a novel EIT-based lung monitoring scheme by using a 3-D digital twin lung model and a deep learning-based image reconstruction algorithm. Unlike the static numerical or experimental simulations used in other data-driven EIT imaging methods, our digital twin lung model incorporates the biomechanical and electrical properties of the lung to generate a more realistic and dynamic dataset. Additionally, the image reconstruction network (IR-Net) is used to learn the prior information in the dataset and accurately reconstruct the conductivity variation within the lungs during respiration. The results indicate that EIT using a guided digital twin and deep learning-based image reconstruction has better accuracy and anti-noise performance compared to traditional EIT. The proposed EIT imaging framework provides a new idea for efficiently creating labeled EIT data and has potential to be used in various data-driven methods for electrical biomedical imaging.</p
Electrical Impedance Tomography Guided by Digital Twins and Deep Learning for Lung Monitoring
In recent years, there has been an increasing interest in applying electrical impedance tomography (EIT) in lung monitoring due to its advantages of being noninvasive, nonionizing, real time, and functional imaging with no harmful side effects. However, the EIT images reconstructed by traditional algorithms suffer from low spatial resolution. This article proposes a novel EIT-based lung monitoring scheme by using a 3-D digital twin lung model and a deep learning-based image reconstruction algorithm. Unlike the static numerical or experimental simulations used in other data-driven EIT imaging methods, our digital twin lung model incorporates the biomechanical and electrical properties of the lung to generate a more realistic and dynamic dataset. Additionally, the image reconstruction network (IR-Net) is used to learn the prior information in the dataset and accurately reconstruct the conductivity variation within the lungs during respiration. The results indicate that EIT using a guided digital twin and deep learning-based image reconstruction has better accuracy and anti-noise performance compared to traditional EIT. The proposed EIT imaging framework provides a new idea for efficiently creating labeled EIT data and has potential to be used in various data-driven methods for electrical biomedical imaging.</p
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